For too long, cybersecurity risk has been measured in meaningless colors. Red, yellow, green heat maps don't tell executives what they need to know: How much could we actually lose? How likely is it? How much should we spend to prevent it?
RisqRadar was built to change that. We provide security leaders with the tools to express cyber risk in the language of business: dollars and probabilities. Using our proprietary QRM™ (Quantified Risk Model) methodology—built on NIST 800-30, Hubbard calibration methods, and Monte Carlo simulation—we help organizations make defensible, data-driven security decisions.
Traditional risk assessments produce qualitative ratings that can't be mathematically combined, compared, or used to calculate return on investment. When the board asks "What's our exposure?" and all you have is a heat map, you're not speaking their language.
RisqRadar uses Monte Carlo simulation to produce probability distributions of potential losses. Instead of "high risk," you can say "We have a $2.4 million expected annual loss from ransomware, with a 10% chance of exceeding $8 million." That's a number the CFO can work with.
QRM (Quantified Risk Model) is our proprietary methodology that combines three proven approaches into a single, practical framework:
QRM decomposes every risk into five measurable factors:
| Factor | What It Measures |
|---|---|
| TP — Threat Probability | How often attackers try |
| ASR — Attack Success Rate | How often they succeed |
| IS — Impact Severity | Direct costs when they do |
| CI — Cascading Impact | Secondary costs that follow |
| IM — Impact Multiplier | Probability of those secondaries |
The result is Annual Loss Expectancy (ALE)—a defensible dollar amount your board can act on. Not "high risk." Not a heat map. A number.
Here's what most risk tools miss: garbage in, garbage out. If your estimates are overconfident—and research shows most people are dramatically overconfident—then even the best simulation engine will produce misleading results.
That's why RisqRadar requires calibration training before you create risk assessments. Our AI Calibration Coach detects cognitive biases like anchoring and overconfidence, then provides personalized exercises to improve your estimation accuracy. When you say 90% confident, you should actually be right 90% of the time.
RisqRadar includes five AI-powered features designed to guide your analysis—not replace your judgment:
The AI provides benchmarks and guidance, but never gives you a single "correct" answer. You remain in control of your risk assessments.
We believe in measuring what matters. Gut feelings aren't good enough for decisions that could cost millions.
Calibration training isn't just a feature—it's mandatory. We fix overconfidence before accepting estimates.
Every recommendation should be backed by data, methodology, and clear reasoning that auditors can verify.
Built on NIST 800-30, our methodology provides federal framework compatibility and regulatory alignment.
Our AI provides benchmarks and validation, but never replaces your judgment. You stay in control.
Quantitative risk analysis shouldn't require a PhD or $200K in consulting fees. We make it accessible to all.
RisqRadar was founded by security professionals and risk analysts who were frustrated with the limitations of traditional risk assessment methods. We've lived the pain of trying to justify security budgets with nothing but heat maps, and we built the tool we wished we had.
Our team combines expertise in cybersecurity, quantitative risk analysis, decision science, and software engineering to bring you a platform that's both scientifically rigorous and practically useful—at a fraction of the cost of enterprise alternatives.